Browsing by Author "Zhao, Ye"

Now showing 1 - 4 of 4
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    GTMapLens: Interactive Lens for Geo-Text Data Browsing on Map
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Ma, Chao; Zhao, Ye; AL-Dohuki, Shamal; Yang, Jing; Ye, Xinyue; Kamw, Farah; Amiruzzaman, Md; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Data containing geospatial semantics, such as geotagged tweets, travel blogs, and crime reports, associates natural language texts with geographical locations. This paper presents a lens-based visual interaction technique, GTMapLens, to flexibly browse the geo-text data on a map. It allows users to perform dynamic focus+context exploration by using movable lenses to browse geographical regions, find locations of interest, and perform comparative and drill-down studies. Geo-text data is visualized in a way that users can easily perceive the underlying geospatial semantics along with lens moving. Based on a requirement analysis with a cohort of multidisciplinary domain experts, a set of lens interaction techniques are developed including keywords control, path management, context visualization, and snapshot anchors. They allow users to achieve a guided and controllable exploration of geo-text data. A hierarchical data model enables the interactive lens operations by accelerated data retrieval from a geo-text database. Evaluation with real-world datasets is presented to show the usability and effectiveness of GTMapLens.
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    Interactive Visualization of AI-based Speech Recognition Texts
    (The Eurographics Association, 2020) Wu, Tsung Heng; Zhao, Ye; Amiruzzaman, Md; Turkay, Cagatay and Vrotsou, Katerina
    Speech recognition technology has achieved impressive success recently with AI techniques of deep learning networks. Speechto- text tools are becoming prevalent in many social applications such as field surveys. However, the speech transcription results are far from perfection for direct use in these applications by domain scientists and practitioners, which prevents the users from fully leveraging the AI tools. In this paper, we show interactive visualization can play important roles in post-AI understanding, editing, and analysis of speech recognition results by presenting specified task characterization and case examples.
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    A Visual Designer of Layer-wise Relevance Propagation Models
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Huang, Xinyi; Jamonnak, Suphanut; Zhao, Ye; Wu, Tsung Heng; Xu, Wei; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
    Layer-wise Relevance Propagation (LRP) is an emerging and widely-used method for interpreting the prediction results of convolutional neural networks (CNN). LRP developers often select and employ different relevance backpropagation rules and parameters, to compute relevance scores on input images. However, there exists no obvious solution to define a ''best'' LRP model. A satisfied model is highly reliant on pertinent images and designers' goals. We develop a visual model designer, named as VisLRPDesigner, to overcome the challenges in the design and use of LRP models. Various LRP rules are unified into an integrated framework with an intuitive workflow of parameter setup. VisLRPDesigner thus allows users to interactively configure and compare LRP models. It also facilitates relevance-based visual analysis with two important functions: relevance-based pixel flipping and neuron ablation. Several use cases illustrate the benefits of VisLRPDesigner. The usability and limitation of the visual designer is evaluated by LRP users.
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    Visually Analyzing Latent Accessibility Clusters of Urban POIs
    (The Eurographics Association, 2019) Kamw, Farah; AL-Dohuki, Shamal; Zhao, Ye; Yang, Jing; Ye, Xinyue; Chen, Wei; Landesberger, Tatiana von and Turkay, Cagatay
    Accessibility of urban POIs (Points of Interest) is a key topic in a variety of urban sciences and applications as it reflects inherent city design, transportation, and population flow features. Isochrone maps and other techniques have been used to identify and display reachable regions from given POIs. In addition, domain experts further want to study the distribution effects of accessibility in the urban space such as finding spatial regions that have different accessibility patterns. Such patterns can be manifested by clustering POIs based on their accessibility of different time periods under different traffic conditions. In this paper, we present a visualization system that helps users to find and visualize Latent Accessibility Clusters (LACs) of POIs. The LACs discover temporally changing urban sub-regions (including nearby POIs) with disparate accessibilities at different times. LACs are computed by a POIGraph which connects POIs into a graph structure by extending the dual road network of the corresponding city. The LAC computation is facilitated by graph traversal over the POIGraph. By visualizing the LAC regions on the map, users can visually study the hidden patterns of spatial accessibility. It can contribute to urban transportation, planning, business, and related social sciences.